Multi-Gene Genetic Programming for Short Term Load Forecasting
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Ghareeb:2013:EPECS,
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author = "W. T. Ghareeb and E. F. {El Saadany}",
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booktitle = "3rd International Conference on Electric Power and
Energy Conversion Systems (EPECS 2013)",
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title = "Multi-Gene Genetic Programming for Short Term Load
Forecasting",
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year = "2013",
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month = "2-4 " # oct,
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keywords = "genetic algorithms, genetic programming, Short-term
load forecasting, multi-gene genetic programming,
radial basis function",
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DOI = "doi:10.1109/EPECS.2013.6713061",
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abstract = "The Short Term Load Forecasting (STLF) plays a
critical role in power system operation. The accuracy
of the STLF is very important since it affects the
generation scheduling and the electricity prices and
hence an accurate STLF method should be used. This
paper presents a new variant of genetic programming
namely: Multi-Gene Genetic Programming (MGGP) for the
problem of STLF. In order to demonstrate this technique
capability, the MGGP has been compared with the RBF
network and the standard single-gene Genetic
Programming (GP) in terms of the forecasting accuracy.
The data used in this study is a real data set of the
Egyptian electrical network. The weather factors
represented by the minimum and the maximum daily
temperature have been included in this study. The MGGP
has successfully predicted the future load with high
accuracy compared to that of the Radial Basis Function
(RBF) network and that of the standard single-gene
Genetic Programming (GP).",
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notes = "Also known as \cite{6713061}",
- }
Genetic Programming entries for
Wael Taha Ghareeb
Ehab El-Saadany
Citations